Statistical Power in Plant Pathology Research

Analysis of the Use of Sample Size and Effect Size Calculations in a Temporomandibular Disorders Randomised Controlled Trial-Short Narrative Review

BACKGROUND/OBJECTIVES

Temporomandibular disorder (TMD) is the term used to describe a pathology (dysfunction and pain) in the masticatory muscles and temporomandibular joint (TMJ). There is an apparent upward trend in the publication of dental research and a need to continually improve the quality of research. Therefore, this study was conducted to analyse the use of sample size and effect size calculations in a TMD randomised controlled trial.

METHODS

The period was restricted to the full 5 years, i.e., papers published in 2019, 2020, 2021, 2022, and 2023. The filter article type-"Randomized Controlled Trial" was used. The studies were graded on a two-level scale: 0-1. In the case of 1, sample size (SS) and effect size (ES) were calculated.

RESULTS

In the entire study sample, SS was used in 58% of studies, while ES was used in 15% of studies.

CONCLUSIONS

Quality should improve as research increases. One factor that influences quality is the level of statistics. SS and ES calculations provide a basis for understanding the results obtained by the authors. Access to formulas, online calculators and software facilitates these analyses. High-quality trials provide a solid foundation for medical progress, fostering the development of personalized therapies that provide more precise and effective treatment and increase patients' chances of recovery. Improving the quality of TMD research, and medical research in general, helps to increase public confidence in medical advances and raises the standard of patient care.

Using design of experiments to guide genetic optimization of engineered metabolic pathways

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

ONE-SENTENCE SUMMARY

This is a review of literature related to applying Design of Experiments for genetic optimization.

High Levels of Insensitivity to Phosphonate Fungicides in Pseudoperonospora humuli

Phosphonate (phosphite; HPO₃^-2) is fungicidal against oomycetes and certain other organisms. The Fungicide Resistance Action Committee has deemed phosphonate to be at low risk of resistance development, and reduced sensitivity to phosphonate has been reported only occasionally in plant pathogens. Reduced sensitivity to the fungicide fosetyl-Al was documented in the hop downy mildew pathogen, Pseudoperonospora humuli, in the early 2000s, but disease caused by insensitive isolates could still be managed commercially if the fungicide rate was doubled from 2.24 to 4.48 kg/ha. In this research, we document the occurrence of isolates of P. humuli in Oregon that possess even higher levels of insensitivity to fosetyl-Al and other phosphonate fungicides. The median estimated effective concentration required to reduce infection by 50% (EC₅₀) for isolates collected from two farms reporting disease control failures was 2.7% (vol/vol) phosphonate (range = 1.6 to 164.2), which was 1.6 times (range = 0.9 to 96.0) the maximum labeled rate of the phosphonate fungicide utilized. In contrast, the median EC₅₀ for isolates obtained from experimental plots that have received only a single application of a phosphonate fungicide was 0.6% (vol/vol) phosphonate (range = 0.11 to 2.3) or 0.3 times the maximum allowable rate. Sensitivity of isolates to a phosphorous acid fungicide, fosetyl-Al, and a plant nutrient product containing an unspecified level of phosphorous acid were linearly related. Insensitivity to the maximum allowable rate of a phosphorous acid fungicide was widespread within and among hop farms in Oregon. Among 54 isolates assayed for phosphonate insensitivity, 96% had EC₅₀ values that exceeded the maximum allow rate of the fungicide used in the assays. Field studies conducted in 2 years further demonstrated that a phosphorous fungicide, a nutrient product containing phosphorous acid, and fosetyl-Al failed to provide commercially acceptable suppression of downy mildew when applied at the maximum allowable rates and even double these rates, whereas fungicides with different modes of action provided 91% or greater disease control. The whole of this research indicates that P. humuli has been selected to tolerate fosetyl-Al and other phosphonate fungicides at rates four times greater than those used earlier to obtain satisfactory suppression of downy mildew. This finding has implications for management of the disease not only in Oregon but also, in other production regions should insensitive isolates be introduced on infected planting material.

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.